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arXiv · 2512.23365

SpatialMosaic: A Multiview VLM Dataset for Partial Visibility

Kanghee Lee·Jungi Hong·Sion Lee·Injae Lee·Minseok Kwak·Kwonyoung Ryu·Jaesik Park
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Abstract

Recent progress in Multimodal Large Language Models (MLLMs) has enabled 3D scene understanding and spatial reasoning directly from multi-view images, without requiring explicit 3D reconstructions. Nevertheless, key challenges that frequently arise in real-world environments, such as partial visibility, occlusion, and low-overlap conditions that require reasoning from fragmented visual cues, remain under-explored. To address these limitations, we propose a scalable multi-view data generation and annotation pipeline that constructs realistic spatial reasoning QAs, resulting in SpatialMosaic, a comprehensive instruction-tuning dataset with 2M QA pairs. We further introduce SpatialMosaic-Bench, a challenging benchmark for evaluating multi-view spatial reasoning under complex and diverse scenarios, consisting of 1M QA pairs across 11 tasks with both multiple-choice and numerical-answer formats. Our dataset spans both indoor and outdoor scenes, enabling comprehensive evaluation across diverse real-world scenarios. In addition, we provide a practical baseline for multi-view settings by integrating geometry encoders into VLMs for improved cross-view consistency and spatial grounding. Extensive experiments demonstrate that our dataset effectively enhances spatial reasoning under challenging multi-view conditions, validating the effectiveness of our data generation pipeline in constructing realistic and challenging QAs.

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Kanghee Lee, Jungi Hong, Sion Lee, Injae Lee, Minseok Kwak, Kwonyoung Ryu, Jaesik Park. 2025-12-29. SpatialMosaic: A Multiview VLM Dataset for Partial Visibility. https://arxiv.org/abs/2512.23365

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